Nisum developed a framework utilizing an AI-driven approach to improve forecast accuracy.
The client has seen improvement in forecast accuracy, resulting in:
- 25% reduction in execution time for forecast processing
- 25% increase in accuracy for accurate order delivery date (AODD)
A Fortune 500 premium goods retailer had a manual process for forecasting orders which provided an inaccurate quantity of orders, leading to:
- Poor resource planning due to inaccurate forecasts, leading to:
- The inability to meet peak holiday capacity
- Inaccurate order delivery dates due to unavailable seasonal forecasts
Nisum developed a framework for effective resource optimization using KPIs such as Site Availability, Service Violations, Shift Coverage. Nisum also used an AI-driven approach to handle data; a high accuracy iterative model was developed, tested, and fitted, leading to:
- Faster forecast processing with improved downtime tracking and enhanced availability across the ecosystem by consolidating historical data as well as additional information for effective forecasting.
- Using Exploratory Data Analysis (EDA) for missing values and outlier treatment
- Improved AODD with improved forecasting of resources using feature engineering. They derived features with impact on response variables to create trend, seasonal, and cyclic variations of forecasts for optimal simulations.
- Increased forecast accuracy by tracking forecast accuracy and model performance periodically using accuracy metrics such as MAPE, MSE, RMSE, and R-Square.
Feel free to contact us for more information on how Nisum can drive results for your company.